Task Orchestrator

A Model Context Protocol server that transforms how you work with AI by automatically documenting every decision, implementation, and test as you build. Think of it as the memory layer for AI-assisted development that ensures no context is ever lost.

The MCP Task Orchestrator provides intelligent task orchestration, specialized AI roles, and persistent memory for AI-assisted development. Built with Clean Architecture principles, it automatically detects project structure and saves artifacts appropriately.

Document Type: Project Overview & User Guide
Target Audience: Developers using MCP clients (Claude Desktop, Cursor, VS Code, etc.)
Prerequisites: Python 3.8+, MCP-compatible client
Last Updated: 2025-01-13

- Documentation Automation: Every task generates comprehensive, searchable artifacts
- Specialist AI Roles: Architect, Implementer, Tester, Reviewer, Documenter, and more
- Persistent Memory: Never lose context - all decisions and implementations are preserved
- Workspace Awareness: Automatically detects project structure and saves artifacts appropriately
- Template System: 13 tools for creating reusable task templates
- Clean Architecture: Built with modern software design principles
- Universal MCP Compatibility: Works across Claude Desktop, Cursor, Windsurf, VS Code + extensions

- Python 3.8+
- One or more MCP clients (Claude Desktop, Cursor IDE, Windsurf, or VS Code with extensions)
- Install:pip install mcp-task-orchestrator
- Configure: Add to your MCP client configuration
- Use: "Initialize task orchestrator session and help me build a REST API"

"Initialize a new orchestration session and plan a Python script for processing CSV files"

See theQuick Start Guidefor detailed setup instructions.

User: "Build a Python web scraper for news articles" Claude: [Provides a single, basic response with minimal code]

You get structured specialist workflows:

User: "Build a Python web scraper for news articles" Step 1: Architect Role ├── System design with rate limiting and error handling ├── Technology selection (requests vs scrapy) ├── Data structure planning └── Scalability considerations Step 2: Implementer Role ├── Core scraping logic implementation ├── Error handling and retries ├── Data parsing and cleaning └── Configuration management Step 3: Tester Role ├── Unit tests for core functions ├── Integration tests with live sites ├── Error condition testing └── Performance validation Step 4: Documenter Role ├── Usage documentation ├── API reference ├── Configuration guide └── Troubleshooting guide Result: Complete implementation with: ✓ Error handling patterns ✓ Test coverage ✓ Documentation ✓ Best practices

Each step provides specialist context and expertise rather than generic responses.

- LLM-powered task decomposition: Automatically breaks complex projects into logical subtasks
- Specialist AI roles: Architect, Implementer, Debugger, Documenter with domain-specific expertise
- Automated maintenance: Built-in cleanup, optimization, and health monitoring
- Task persistence: SQLite database with automatic recovery and archival
- Artifact management: Prevents context limits with intelligent file storage
- Workspace intelligence: Automatically detects Git repositories, project files, and saves artifacts appropriately
- Customizable roles: Edit.task_orchestrator/roles/project_roles.yamlto adapt roles for your project
- Single-session completion: Finish complex projects in one conversation
- Smart artifact placement: Files are saved relative to your project root, not random locations

The universal installer provides comprehensive support for all major MCP clients with flexible installation options.

Quick Install - Auto-detect all clients:

# Download and run the universal installer git clone https://github.com/EchoingVesper/mcp-task-orchestrator.git cd mcp-task-orchestrator python install.py # Auto-detects and configures all compatible MCP clients # Restart your MCP clients - the orchestrator tools will be available automatically

PyPI Installation with Manual Configuration:

# Install from PyPI pip install mcp-task-orchestrator # Then configure your MCP client manually (see configuration section below)

# Configure specific clients only python install.py --clients claude,cursor # Skip MCP configuration entirely (manual setup) python install.py --no-clients # Development installation with all tools python install.py --dev # Install in user directory python install.py --user

# Force PyPI installation even in development python install.py --source pypi # Install specific version python install.py --version 2.0.0 # Install from git repository python install.py --git https://github.com/EchoingVesper/mcp-task-orchestrator.git # Install in custom virtual environment python install.py --venv /path/to/venv # Force overwrite existing installation python install.py --force

For Externally Managed Environments (WSL, Ubuntu 23.04+):

# Create virtual environment first python -m venv mcp-orchestrator-env source mcp-orchestrator-env/bin/activate # Linux/WSL/macOS # OR: mcp-orchestrator-env\Scripts\activate # Windows # Clone and install git clone https://github.com/EchoingVesper/mcp-task-orchestrator.git cd mcp-task-orchestrator python install.py --venv ../mcp-orchestrator-env

# Install via pipx for isolation pipx install mcp-task-orchestrator # Manual MCP configuration required (see configuration section)

- ✅Zero vulnerabilities: All 38 security issues resolved
- ✅Cross-platform: Windows, macOS, Linux support
- ✅Multi-client: Claude Desktop, Cursor, Windsurf, VS Code, Zed, Claude Code
- ✅Automatic backups: Configuration protection and rollback
- ✅Performance: < 5 seconds installation, < 50MB memory usage
- ✅Validation: Comprehensive post-installation verification

# View installation help and options python install.py --help # Check installation status python install.py --status # Force reconfiguration if already installed python install.py --force # Test dry-run mode to see what would be done python install.py --dry-run --verbose

- Claude Code not detected: Ensure Claude Code CLI is installed andclaude --versionworks
- Config file not found: Make sure the MCP client is installed and has been run at least once
- Permission errors: Check file permissions for config directories
- Already configured: Use--forceflag to overwrite existing configurations

- Claude Desktop: Works globally across multiple projects using dynamic detection
- Claude Code: Works automatically with per-project detection for best experience
- Windsurf/Cursor: Automatically detect project context when opened in project folders

For comprehensive troubleshooting, see[Installation Troubleshooting Guide.

"Initialize a new orchestration session and plan a Python script for processing CSV files"

The orchestrator follows a systematic five-step process:
- Workspace Detection- Automatically identifies your project type and root directory
- Task Analysis- LLM analyzes your request and creates structured subtasks
- Task Planning- Organizes subtasks with dependencies and complexity assessment
- Specialist Execution- Each subtask runs with role-specific context and expertise
- Result Synthesis- Combines outputs into a comprehensive solution with workspace-aware artifact placement

Core orchestration tools for task management and execution:

The orchestrator includes intelligent maintenance capabilities:

…